Import modules

In [1]:
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [2]:
!pip install tensorflow --upgrade
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In [0]:
import tensorflow as tf

# Set different random seeds for comparison
tf.random.set_seed(0)
#tf.random.set_seed(2019)

from tensorflow.python.keras.preprocessing import image as kp_image
from tensorflow.python.keras import models 
from tensorflow.python.keras import losses
from tensorflow.python.keras import layers
from tensorflow.python.keras import backend as K
In [0]:
import IPython.display

import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (12,12)
mpl.rcParams['axes.grid'] = False

import numpy as np
from PIL import Image
import time
import functools

Input Content and Style Images

In [0]:
# Set up image path
# Use different types of content and style images
content_path1 = '/content/drive/My Drive/AML Project/people.jpg'
content_path2 = '/content/drive/My Drive/AML Project/puppy-dog.jpg'
content_path3 = '/content/drive/My Drive/AML Project/Shanghai.jpg'
content_path4 = '/content/drive/My Drive/AML Project/dancing.jpg'

style_path1   = '/content/drive/My Drive/AML Project/picasso.jpg'
style_path2   = '/content/drive/My Drive/AML Project/great_wave.jpg'
style_path3   = '/content/drive/My Drive/AML Project/Van_Gogh.jpg'
style_path4   = '/content/drive/My Drive/AML Project/cezanne.jpg'
In [0]:
def load_img(path_to_img):
  max_dim = 512
  img = Image.open(path_to_img)
  img_size = max(img.size)
  scale = max_dim/img_size
  img = img.resize((round(img.size[0]*scale), round(img.size[1]*scale)), Image.ANTIALIAS)
  
  img = kp_image.img_to_array(img)

  img = np.expand_dims(img, axis=0)
  return img
In [0]:
def imshow(img, title=None):
  # Remove the batch dimension
  out = np.squeeze(img, axis=0) 
  out = out.astype('uint8')
  plt.imshow(out)
  if title is not None:
    plt.title(title)
  plt.imshow(out)
In [8]:
# Show the input content and style images
plt.figure(figsize=(12,12))

content_image1 = load_img(content_path1).astype('uint8')
style_image1 = load_img(style_path1).astype('uint8')

content_image2 = load_img(content_path2).astype('uint8')
style_image2 = load_img(style_path2).astype('uint8')

content_image3 = load_img(content_path3).astype('uint8')
style_image3 = load_img(style_path3).astype('uint8')

content_image4 = load_img(content_path4).astype('uint8')
style_image4 = load_img(style_path4).astype('uint8')

plt.subplot(4, 2, 1)
imshow(content_image1, 'Content Image1')

plt.subplot(4, 2, 2)
imshow(style_image1, 'Style Image1')

plt.subplot(4, 2, 3)
imshow(content_image2, 'Content Image2')

plt.subplot(4, 2, 4)
imshow(style_image2, 'Style Image2')

plt.subplot(4, 2, 5)
imshow(content_image3, 'Content Image3')

plt.subplot(4, 2, 6)
imshow(style_image3, 'Style Image3')

plt.subplot(4, 2, 7)
imshow(content_image4, 'Content Image4')

plt.subplot(4, 2, 8)
imshow(style_image4, 'Style Image4')
plt.show()

Preprocess data

In [0]:
def load_and_process_img(path_to_img):
  img = load_img(path_to_img)
  # Preprocess raw images to make it suitable to be used by pre-trained VGG19 model
  output = tf.keras.applications.vgg19.preprocess_input(img)
  return output
In [0]:
def deprocess_img(processed_img):
  x = processed_img.copy()
  if len(x.shape) == 4:
    x = np.squeeze(x, 0)
  assert len(x.shape) == 3, ("Input to deprocess image must be an image of "
                                      "dimension [1, height, width, channel] or [height, width, channel]")
  if len(x.shape) != 3:
    raise ValueError("Invalid input to deprocessing image")
  
  # Perform the inverse of the preprocessiing step
  # VGG networks are trained on image with each channel normalized by mean = [103.939, 116.779, 123.68] and with channels BGR
  x[:, :, 0] += 103.939
  x[:, :, 1] += 116.779
  x[:, :, 2] += 123.68
  x = x[:, :, ::-1]

  x = np.clip(x, 0, 255).astype('uint8')
  return x

Define content and style representations

In [11]:
# Use weights of pre-trained VGG19 to build our neural network
vgg = tf.keras.applications.VGG19(include_top=False, weights="/content/drive/My Drive/AML Project/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5")

for layer in vgg.layers:
  print(layer.name)
input_1
block1_conv1
block1_conv2
block1_pool
block2_conv1
block2_conv2
block2_pool
block3_conv1
block3_conv2
block3_conv3
block3_conv4
block3_pool
block4_conv1
block4_conv2
block4_conv3
block4_conv4
block4_pool
block5_conv1
block5_conv2
block5_conv3
block5_conv4
block5_pool
In [0]:
# List of Content and Style layers to be considered for calculation of Content and Style Loss
# Content layer where will pull our feature maps
# Try different Content layers to see the difference
# In the reference paper, the author stated that "We therefore refer to the feature responses in higher layers of the network as the content representation."
content_layers1 = ['block5_conv2'] 
content_layers2 = ['block5_conv3']
content_layers3 = ['block4_conv2']
content_layers4 = ['block3_conv2']   

# Style layer of interest
style_layers = ['block1_conv1',
                     'block2_conv1',
                     'block3_conv1', 
                     'block4_conv1', 
                     'block5_conv1']

num_content_layers1 = len(content_layers1)
num_content_layers2 = len(content_layers2)
num_content_layers3 = len(content_layers3)
num_content_layers4 = len(content_layers4)
num_style_layers = len(style_layers)

Build Model

In [0]:
def get_model(content_layers = content_layers1):
  """ Creates our model with access to intermediate layers."""
  # Load our model and use pre-trained VGG19 weights
  vgg = tf.keras.applications.vgg19.VGG19(include_top=False, weights="/content/drive/My Drive/AML Project/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5")
  vgg.trainable = False
  # Get output layers corresponding to style and content layers 
  style_outputs = [vgg.get_layer(name).output for name in style_layers]
  content_outputs = [vgg.get_layer(name).output for name in content_layers]
  model_outputs = style_outputs + content_outputs
  # Build model 
  model = tf.keras.Model([vgg.input], model_outputs)
  return model

Define and create loss functions (both style and content loss)

Style Loss

In [0]:
def gram_matrix(input_tensor):
  # Since the input tensor is a 3D array of size height * width * channels, we reshape it to a 2D array of (height * width) * channels
  channels = int(input_tensor.shape[-1])
  # -1 means the size of height * width
  a = tf.reshape(input_tensor, [-1, channels])
  b = tf.reshape(input_tensor, [-1, channels])
  # Gram matrix G_ij is the inner product, and G_ij has dimension N_l * N_l
  # Thus, gram matrix is the inner product of the transpose matrix of A and A, which will have size N_l * N_l
  gram = tf.matmul(a, b, transpose_a=True)
  return gram
In [0]:
def get_style_loss(base_style, gram_target):
  # Get height, width, and num filters of each layer
  # We scale the loss at a given layer by the size of the feature map and the number of filters
  height, width, channels = base_style.get_shape().as_list()
  gram_style = gram_matrix(base_style)
  # gram_target will be processed as a gram_matrix in the run_style_transfer step
  # Style Loss = 1/(4 * (N_l * M_l)^2) * sum_i,j((G_ij - A_ij)^2), where M_l is the height times the width of the feature map
  return tf.reduce_sum((gram_style - gram_target)**2)/(4*(channels * height * width)**2)

Content Loss

In [0]:
def get_content_loss(content, target):
  # Content Loss = 1/2 * sum_i,j((F_ij - P_ij)^2)
  return tf.reduce_sum((content - target)**2)/2 

Total Loss

In [0]:
def get_feature_representations(model, content_path, style_path):
  # Load our images
  content_image = load_and_process_img(content_path)
  style_image = load_and_process_img(style_path)
  
  style_outputs = model(style_image)
  content_outputs = model(content_image)
  
  # Get the style and content feature representations from our model  
  style_features = [style_layer[0] for style_layer in style_outputs[:num_style_layers]]
  content_features = [content_layer[0] for content_layer in content_outputs[num_style_layers:]]
  return style_features, content_features
In [0]:
def compute_loss(model, num_content_layers, loss_weights, init_image, gram_style_features, content_features):
  style_weight, content_weight = loss_weights

  generated = model(init_image)
  style_output_features = generated[:num_style_layers]
  content_output_features = generated[num_style_layers:]
  
  style_score = 0
  content_score = 0

  # Accumulate style losses from all layers
  # We use equal weights for each content and style loss layer
  weight_per_style_layer = 1.0 / float(num_style_layers)
  for target_style, comb_style in zip(gram_style_features, style_output_features):
    style_score += weight_per_style_layer * get_style_loss(comb_style[0], target_style)
    
  # Accumulate content losses from all layers 
  weight_per_content_layer = 1.0 / float(num_content_layers)
  for target_content, comb_content in zip(content_features, content_output_features):
    content_score += weight_per_content_layer * get_content_loss(comb_content[0], target_content)

  # Get total loss
  loss = style_weight * style_score + content_weight * content_score 
  return loss, style_score, content_score
In [0]:
def compute_grads(cfg):
  # Use tf.GradientTape() to compute the gradient
  with tf.GradientTape() as tape: 
    all_loss = compute_loss(**cfg)
  # Compute gradients
  total_loss = all_loss[0]
  return tape.gradient(total_loss, cfg['init_image']), all_loss

Style Transfer

In [0]:
def run_style_transfer(content_path, 
                                style_path,
                                content_layers=content_layers1,
                                num_iterations=1000,
                                content_weight=1, 
                                style_weight=1e3): 
  
  # Part of the function was inspired by the reference article published by TensorFlow
  display_num = 1  
  #display_num = 10
  num_content_layers = len(content_layers)

  model = get_model(content_layers) 
  for layer in model.layers:
    layer.trainable = False
  
  # Get the style and content feature representations
  style_features, content_features = get_feature_representations(model, content_path, style_path)
  gram_style_features = [gram_matrix(style_feature) for style_feature in style_features]
  
  # Set initial content image
  init_image = load_and_process_img(content_path)
  init_image = tf.Variable(init_image, dtype=tf.float32)
  # Create our optimizer and Adam will work here (The paper suggested L-BFGS but we saw many other referrence still used Adam and Adam also worked)
  opt = tf.keras.optimizers.Adam(learning_rate=9, beta_1=0.99, epsilon=1e-1)

  # For displaying intermediate images 
  iter_count = 1
  
  # Store our best result
  best_loss, best_img = float('inf'), None

  # Create lists to store all three losses
  content_loss = []
  style_loss = []
  total_loss = []
  
  # Create a nice config 
  loss_weights = (style_weight, content_weight)
  cfg = {
      'model': model,
      'num_content_layers': num_content_layers,
      'loss_weights': loss_weights,
      'init_image': init_image,
      'gram_style_features': gram_style_features,
      'content_features': content_features
  }
    
  # For displaying
  plt.figure(figsize=(12, 12))
  num_rows = (num_iterations / display_num) // 5
  start_time = time.time()
  global_start = time.time()
  
  norm_means = np.array([103.939, 116.779, 123.68])
  min_vals = -norm_means
  max_vals = 255 - norm_means

  # Create a list to store all pictures we want in iteration process
  output = []
  iter_list = [100,200,300,400,500,600,700,800,900,1000]

  for i in range(num_iterations):
    grads, all_loss = compute_grads(cfg)
    loss, style_score, content_score = all_loss
    # grads, _ = tf.clip_by_global_norm(grads, 5.0)
    opt.apply_gradients([(grads, init_image)])
    clipped = tf.clip_by_value(init_image, min_vals, max_vals)
    init_image.assign(clipped)
    end_time = time.time() 
    
    if loss < best_loss:
      # Update best loss and best image from total loss
      best_loss = loss
      best_img = init_image.numpy()

    if i % display_num == 0:
      print('Iteration: {}'.format(i))        
      print('Total loss: {:.4e}, ' 
            'style loss: {:.4e}, '
            'content loss: {:.4e}, '
            'time: {:.4f}s'.format(loss, style_score, content_score, time.time() - start_time))
      start_time = time.time()

      content_loss.append(content_score)
      style_loss.append(style_score)
      total_loss.append(loss)

    if (i+1) in iter_list:
      output.append(best_img)

  print('Total time: {:.4f}s'.format(time.time() - global_start))
      
  return best_img, total_loss, style_loss, content_loss, output

Visualization

In [0]:
def show_results(best_img, content_path, style_path, show_large_final=True):
  plt.figure(figsize=(10, 5))
  content = load_img(content_path) 
  style = load_img(style_path)

  plt.subplot(1, 2, 1)
  imshow(content, 'Content Image')

  plt.subplot(1, 2, 2)
  imshow(style, 'Style Image')

  if show_large_final: 
    plt.figure(figsize=(10, 10))
    plt.imshow(deprocess_img(best_img))
    plt.title('Output Image')
    plt.show()

Result

Parameters

Content Image: Shanghai Style Image: The Starry Night Random Seed: 0
Content layer: block3_conv2
iteration: 1000
Content Loss Weight: 1
Style Loss Weight: 1e3
Content-Style Loss Weight Ratio: 1:1000

In [22]:
# Seed 0
import time
start = time.time()

best, best_loss, style_score, content_score, output = run_style_transfer(content_path3, style_path3, content_layers=content_layers4, num_iterations=1000, content_weight=1, style_weight = 1e3)

show_results(best, content_path3, style_path3)
  
end = time.time()
print("Total time: {:.1f}".format(end-start))
Iteration: 0
Total loss: 6.2955e+12, style loss: 6.2955e+09, content loss: 0.0000e+00, time: 3.8978s
Iteration: 1
Total loss: 2.1630e+12, style loss: 2.1324e+09, content loss: 3.0632e+10, time: 3.9728s
Iteration: 2
Total loss: 2.4872e+12, style loss: 2.4260e+09, content loss: 6.1202e+10, time: 3.9835s
Iteration: 3
Total loss: 1.8947e+12, style loss: 1.8253e+09, content loss: 6.9429e+10, time: 3.9834s
Iteration: 4
Total loss: 1.6219e+12, style loss: 1.5467e+09, content loss: 7.5269e+10, time: 4.0218s
Iteration: 5
Total loss: 1.2543e+12, style loss: 1.1728e+09, content loss: 8.1492e+10, time: 4.0237s
Iteration: 6
Total loss: 1.1217e+12, style loss: 1.0339e+09, content loss: 8.7705e+10, time: 3.9792s
Iteration: 7
Total loss: 1.0310e+12, style loss: 9.3871e+08, content loss: 9.2315e+10, time: 3.9621s
Iteration: 8
Total loss: 9.4406e+11, style loss: 8.4831e+08, content loss: 9.5752e+10, time: 3.9513s
Iteration: 9
Total loss: 9.1374e+11, style loss: 8.1458e+08, content loss: 9.9159e+10, time: 3.9844s
Iteration: 10
Total loss: 8.6276e+11, style loss: 7.5964e+08, content loss: 1.0312e+11, time: 3.9608s
Iteration: 11
Total loss: 8.0270e+11, style loss: 6.9513e+08, content loss: 1.0757e+11, time: 3.9766s
Iteration: 12
Total loss: 7.9375e+11, style loss: 6.8182e+08, content loss: 1.1193e+11, time: 3.9669s
Iteration: 13
Total loss: 8.0174e+11, style loss: 6.8637e+08, content loss: 1.1538e+11, time: 3.9559s
Iteration: 14
Total loss: 7.7441e+11, style loss: 6.5680e+08, content loss: 1.1761e+11, time: 3.9448s
Iteration: 15
Total loss: 7.4883e+11, style loss: 6.2970e+08, content loss: 1.1913e+11, time: 3.9975s
Iteration: 16
Total loss: 7.4385e+11, style loss: 6.2325e+08, content loss: 1.2060e+11, time: 3.9867s
Iteration: 17
Total loss: 7.2604e+11, style loss: 6.0364e+08, content loss: 1.2240e+11, time: 3.9561s
Iteration: 18
Total loss: 6.9078e+11, style loss: 5.6622e+08, content loss: 1.2456e+11, time: 3.9435s
Iteration: 19
Total loss: 6.6681e+11, style loss: 5.3995e+08, content loss: 1.2686e+11, time: 3.9786s
Iteration: 20
Total loss: 6.6371e+11, style loss: 5.3477e+08, content loss: 1.2893e+11, time: 3.9890s
Iteration: 21
Total loss: 6.5827e+11, style loss: 5.2784e+08, content loss: 1.3043e+11, time: 4.0018s
Iteration: 22
Total loss: 6.3770e+11, style loss: 5.0640e+08, content loss: 1.3130e+11, time: 3.9759s
Iteration: 23
Total loss: 6.1908e+11, style loss: 4.8727e+08, content loss: 1.3181e+11, time: 3.9428s
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Total loss: 6.4013e+10, style loss: 1.2674e+07, content loss: 5.1339e+10, time: 3.8932s
Iteration: 887
Total loss: 6.3998e+10, style loss: 1.2671e+07, content loss: 5.1327e+10, time: 3.9801s
Iteration: 888
Total loss: 6.3983e+10, style loss: 1.2668e+07, content loss: 5.1315e+10, time: 4.0026s
Iteration: 889
Total loss: 6.3969e+10, style loss: 1.2665e+07, content loss: 5.1304e+10, time: 3.9333s
Iteration: 890
Total loss: 6.3954e+10, style loss: 1.2661e+07, content loss: 5.1293e+10, time: 3.9534s
Iteration: 891
Total loss: 6.3939e+10, style loss: 1.2659e+07, content loss: 5.1281e+10, time: 3.9443s
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Total loss: 6.3925e+10, style loss: 1.2656e+07, content loss: 5.1269e+10, time: 3.9432s
Iteration: 893
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Iteration: 894
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Iteration: 896
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Iteration: 897
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Iteration: 898
Total loss: 6.3839e+10, style loss: 1.2641e+07, content loss: 5.1198e+10, time: 3.9587s
Iteration: 899
Total loss: 6.3825e+10, style loss: 1.2639e+07, content loss: 5.1187e+10, time: 3.9708s
Iteration: 900
Total loss: 6.3811e+10, style loss: 1.2636e+07, content loss: 5.1175e+10, time: 3.9580s
Iteration: 901
Total loss: 6.3797e+10, style loss: 1.2634e+07, content loss: 5.1163e+10, time: 4.0098s
Iteration: 902
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Total loss: 6.3755e+10, style loss: 1.2627e+07, content loss: 5.1128e+10, time: 4.0181s
Iteration: 905
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Iteration: 906
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Iteration: 907
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Iteration: 908
Total loss: 6.3699e+10, style loss: 1.2618e+07, content loss: 5.1081e+10, time: 3.9477s
Iteration: 909
Total loss: 6.3686e+10, style loss: 1.2615e+07, content loss: 5.1071e+10, time: 3.9483s
Iteration: 910
Total loss: 6.3672e+10, style loss: 1.2612e+07, content loss: 5.1060e+10, time: 3.9749s
Iteration: 911
Total loss: 6.3658e+10, style loss: 1.2609e+07, content loss: 5.1050e+10, time: 4.0016s
Iteration: 912
Total loss: 6.3645e+10, style loss: 1.2605e+07, content loss: 5.1039e+10, time: 3.9483s
Iteration: 913
Total loss: 6.3631e+10, style loss: 1.2601e+07, content loss: 5.1030e+10, time: 3.9527s
Iteration: 914
Total loss: 6.3617e+10, style loss: 1.2598e+07, content loss: 5.1020e+10, time: 3.9639s
Iteration: 915
Total loss: 6.3604e+10, style loss: 1.2595e+07, content loss: 5.1009e+10, time: 3.9489s
Iteration: 916
Total loss: 6.3590e+10, style loss: 1.2592e+07, content loss: 5.0998e+10, time: 3.9766s
Iteration: 917
Total loss: 6.3577e+10, style loss: 1.2589e+07, content loss: 5.0988e+10, time: 3.9499s
Iteration: 918
Total loss: 6.3564e+10, style loss: 1.2585e+07, content loss: 5.0979e+10, time: 3.9575s
Iteration: 919
Total loss: 6.3550e+10, style loss: 1.2581e+07, content loss: 5.0969e+10, time: 3.9585s
Iteration: 920
Total loss: 6.3537e+10, style loss: 1.2579e+07, content loss: 5.0958e+10, time: 3.9773s
Iteration: 921
Total loss: 6.3524e+10, style loss: 1.2577e+07, content loss: 5.0947e+10, time: 3.9741s
Iteration: 922
Total loss: 6.3511e+10, style loss: 1.2574e+07, content loss: 5.0937e+10, time: 3.9981s
Iteration: 923
Total loss: 6.3498e+10, style loss: 1.2571e+07, content loss: 5.0926e+10, time: 4.0220s
Iteration: 924
Total loss: 6.3484e+10, style loss: 1.2569e+07, content loss: 5.0916e+10, time: 4.0061s
Iteration: 925
Total loss: 6.3471e+10, style loss: 1.2567e+07, content loss: 5.0904e+10, time: 3.9761s
Iteration: 926
Total loss: 6.3458e+10, style loss: 1.2565e+07, content loss: 5.0894e+10, time: 3.9758s
Iteration: 927
Total loss: 6.3445e+10, style loss: 1.2562e+07, content loss: 5.0884e+10, time: 3.9753s
Iteration: 928
Total loss: 6.3432e+10, style loss: 1.2559e+07, content loss: 5.0873e+10, time: 4.0002s
Iteration: 929
Total loss: 6.3419e+10, style loss: 1.2557e+07, content loss: 5.0862e+10, time: 3.9618s
Iteration: 930
Total loss: 6.3406e+10, style loss: 1.2556e+07, content loss: 5.0850e+10, time: 3.9563s
Iteration: 931
Total loss: 6.3393e+10, style loss: 1.2553e+07, content loss: 5.0840e+10, time: 3.9648s
Iteration: 932
Total loss: 6.3380e+10, style loss: 1.2550e+07, content loss: 5.0831e+10, time: 3.9425s
Iteration: 933
Total loss: 6.3367e+10, style loss: 1.2547e+07, content loss: 5.0821e+10, time: 3.9472s
Iteration: 934
Total loss: 6.3355e+10, style loss: 1.2545e+07, content loss: 5.0810e+10, time: 3.9607s
Iteration: 935
Total loss: 6.3342e+10, style loss: 1.2543e+07, content loss: 5.0799e+10, time: 4.0462s
Iteration: 936
Total loss: 6.3329e+10, style loss: 1.2540e+07, content loss: 5.0789e+10, time: 4.0187s
Iteration: 937
Total loss: 6.3317e+10, style loss: 1.2538e+07, content loss: 5.0778e+10, time: 4.0221s
Iteration: 938
Total loss: 6.3304e+10, style loss: 1.2536e+07, content loss: 5.0768e+10, time: 3.9674s
Iteration: 939
Total loss: 6.3291e+10, style loss: 1.2535e+07, content loss: 5.0757e+10, time: 3.9501s
Iteration: 940
Total loss: 6.3279e+10, style loss: 1.2532e+07, content loss: 5.0747e+10, time: 3.9889s
Iteration: 941
Total loss: 6.3266e+10, style loss: 1.2529e+07, content loss: 5.0738e+10, time: 3.9973s
Iteration: 942
Total loss: 6.3254e+10, style loss: 1.2526e+07, content loss: 5.0728e+10, time: 4.0260s
Iteration: 943
Total loss: 6.3242e+10, style loss: 1.2524e+07, content loss: 5.0718e+10, time: 4.0050s
Iteration: 944
Total loss: 6.3229e+10, style loss: 1.2522e+07, content loss: 5.0708e+10, time: 3.9738s
Iteration: 945
Total loss: 6.3217e+10, style loss: 1.2519e+07, content loss: 5.0698e+10, time: 3.9440s
Iteration: 946
Total loss: 6.3205e+10, style loss: 1.2516e+07, content loss: 5.0689e+10, time: 3.9456s
Iteration: 947
Total loss: 6.3193e+10, style loss: 1.2513e+07, content loss: 5.0679e+10, time: 3.9352s
Iteration: 948
Total loss: 6.3181e+10, style loss: 1.2512e+07, content loss: 5.0669e+10, time: 3.9641s
Iteration: 949
Total loss: 6.3168e+10, style loss: 1.2510e+07, content loss: 5.0659e+10, time: 3.9505s
Iteration: 950
Total loss: 6.3156e+10, style loss: 1.2507e+07, content loss: 5.0650e+10, time: 3.9644s
Iteration: 951
Total loss: 6.3145e+10, style loss: 1.2504e+07, content loss: 5.0640e+10, time: 3.9570s
Iteration: 952
Total loss: 6.3133e+10, style loss: 1.2503e+07, content loss: 5.0630e+10, time: 3.9805s
Iteration: 953
Total loss: 6.3121e+10, style loss: 1.2501e+07, content loss: 5.0620e+10, time: 3.9751s
Iteration: 954
Total loss: 6.3109e+10, style loss: 1.2499e+07, content loss: 5.0610e+10, time: 3.9620s
Iteration: 955
Total loss: 6.3097e+10, style loss: 1.2496e+07, content loss: 5.0601e+10, time: 3.9663s
Iteration: 956
Total loss: 6.3085e+10, style loss: 1.2493e+07, content loss: 5.0592e+10, time: 3.9867s
Iteration: 957
Total loss: 6.3073e+10, style loss: 1.2492e+07, content loss: 5.0582e+10, time: 4.0766s
Iteration: 958
Total loss: 6.3062e+10, style loss: 1.2489e+07, content loss: 5.0573e+10, time: 4.0043s
Iteration: 959
Total loss: 6.3050e+10, style loss: 1.2486e+07, content loss: 5.0564e+10, time: 3.9550s
Iteration: 960
Total loss: 6.3038e+10, style loss: 1.2483e+07, content loss: 5.0556e+10, time: 3.9889s
Iteration: 961
Total loss: 6.3027e+10, style loss: 1.2480e+07, content loss: 5.0547e+10, time: 4.0073s
Iteration: 962
Total loss: 6.3015e+10, style loss: 1.2478e+07, content loss: 5.0537e+10, time: 3.9726s
Iteration: 963
Total loss: 6.3004e+10, style loss: 1.2475e+07, content loss: 5.0529e+10, time: 4.0423s
Iteration: 964
Total loss: 6.2992e+10, style loss: 1.2472e+07, content loss: 5.0520e+10, time: 4.0334s
Iteration: 965
Total loss: 6.2981e+10, style loss: 1.2469e+07, content loss: 5.0512e+10, time: 3.9876s
Iteration: 966
Total loss: 6.2969e+10, style loss: 1.2467e+07, content loss: 5.0502e+10, time: 3.9988s
Iteration: 967
Total loss: 6.2958e+10, style loss: 1.2465e+07, content loss: 5.0493e+10, time: 4.0168s
Iteration: 968
Total loss: 6.2946e+10, style loss: 1.2462e+07, content loss: 5.0485e+10, time: 4.0320s
Iteration: 969
Total loss: 6.2935e+10, style loss: 1.2459e+07, content loss: 5.0476e+10, time: 4.0289s
Iteration: 970
Total loss: 6.2924e+10, style loss: 1.2456e+07, content loss: 5.0468e+10, time: 4.0242s
Iteration: 971
Total loss: 6.2912e+10, style loss: 1.2454e+07, content loss: 5.0459e+10, time: 4.0228s
Iteration: 972
Total loss: 6.2901e+10, style loss: 1.2450e+07, content loss: 5.0450e+10, time: 4.0044s
Iteration: 973
Total loss: 6.2889e+10, style loss: 1.2447e+07, content loss: 5.0443e+10, time: 4.0245s
Iteration: 974
Total loss: 6.2878e+10, style loss: 1.2444e+07, content loss: 5.0434e+10, time: 3.9677s
Iteration: 975
Total loss: 6.2867e+10, style loss: 1.2442e+07, content loss: 5.0425e+10, time: 3.9527s
Iteration: 976
Total loss: 6.2856e+10, style loss: 1.2440e+07, content loss: 5.0416e+10, time: 3.9611s
Iteration: 977
Total loss: 6.2844e+10, style loss: 1.2437e+07, content loss: 5.0408e+10, time: 3.9745s
Iteration: 978
Total loss: 6.2833e+10, style loss: 1.2434e+07, content loss: 5.0399e+10, time: 3.9911s
Iteration: 979
Total loss: 6.2822e+10, style loss: 1.2432e+07, content loss: 5.0390e+10, time: 3.9938s
Iteration: 980
Total loss: 6.2811e+10, style loss: 1.2431e+07, content loss: 5.0380e+10, time: 4.0199s
Iteration: 981
Total loss: 6.2800e+10, style loss: 1.2429e+07, content loss: 5.0371e+10, time: 4.0059s
Iteration: 982
Total loss: 6.2789e+10, style loss: 1.2426e+07, content loss: 5.0362e+10, time: 3.9942s
Iteration: 983
Total loss: 6.2778e+10, style loss: 1.2425e+07, content loss: 5.0353e+10, time: 3.9756s
Iteration: 984
Total loss: 6.2767e+10, style loss: 1.2424e+07, content loss: 5.0343e+10, time: 3.9849s
Iteration: 985
Total loss: 6.2756e+10, style loss: 1.2422e+07, content loss: 5.0334e+10, time: 3.9413s
Iteration: 986
Total loss: 6.2745e+10, style loss: 1.2420e+07, content loss: 5.0326e+10, time: 3.9411s
Iteration: 987
Total loss: 6.2735e+10, style loss: 1.2417e+07, content loss: 5.0317e+10, time: 3.9101s
Iteration: 988
Total loss: 6.2724e+10, style loss: 1.2415e+07, content loss: 5.0309e+10, time: 3.9587s
Iteration: 989
Total loss: 6.2713e+10, style loss: 1.2413e+07, content loss: 5.0300e+10, time: 3.9131s
Iteration: 990
Total loss: 6.2702e+10, style loss: 1.2411e+07, content loss: 5.0292e+10, time: 3.9288s
Iteration: 991
Total loss: 6.2692e+10, style loss: 1.2408e+07, content loss: 5.0284e+10, time: 3.9510s
Iteration: 992
Total loss: 6.2681e+10, style loss: 1.2407e+07, content loss: 5.0275e+10, time: 3.9203s
Iteration: 993
Total loss: 6.2670e+10, style loss: 1.2406e+07, content loss: 5.0265e+10, time: 3.9316s
Iteration: 994
Total loss: 6.2660e+10, style loss: 1.2404e+07, content loss: 5.0256e+10, time: 3.9562s
Iteration: 995
Total loss: 6.2649e+10, style loss: 1.2402e+07, content loss: 5.0247e+10, time: 3.9121s
Iteration: 996
Total loss: 6.2639e+10, style loss: 1.2399e+07, content loss: 5.0239e+10, time: 3.9331s
Iteration: 997
Total loss: 6.2628e+10, style loss: 1.2398e+07, content loss: 5.0231e+10, time: 3.9071s
Iteration: 998
Total loss: 6.2618e+10, style loss: 1.2396e+07, content loss: 5.0222e+10, time: 3.9318s
Iteration: 999
Total loss: 6.2607e+10, style loss: 1.2393e+07, content loss: 5.0214e+10, time: 3.8856s
Total time: 3938.6745s
<Figure size 864x864 with 0 Axes>
Total time: 3942.4
In [23]:
for i in range(len(output)):
    plt.figure(figsize=(10, 10))
    plt.imshow(deprocess_img(output[i]))
    plt.title(str((i+1)*100) + ' iteration Output Image')
    plt.show()
In [24]:
plt.plot(best_loss)
plt.title('Total Loss')
plt.show()
In [25]:
plt.plot(style_score)
plt.title('Style Loss')
plt.show()
In [26]:
plt.plot(content_score)
plt.title('Content Loss')
plt.show()

By using different random seeds, content and style weight ratios, numbers of iterations and the content layers used in the model, we produced many style transferred pictures and we put many of them in our project final report.

Reference

TensorFlow. (2018, September 27). Neural Style Transfer: Creating Art with Deep Learning using tf.keras and eager execution. Retrieved from https://medium.com/tensorflow/neural-style-transfer-creating-art-with-deep-learning-using-tf-keras-and-eager-execution-7d541ac31398.